code: def _conductance_test_assert(
      self,
      model: Module,
      target_layer: Module,
      test_input: Union[Tensor, Tuple[Tensor, ...]],
      expected_conductance: Union[
          List[List[float]], Tuple[List[List[float]], ...]
      ],
      baselines: BaselineType = None,
      additional_args: Any = None,
  ) -> None:
      cond = LayerConductance(model, target_layer)
      self.assertTrue(cond.multiplies_by_inputs)
      for internal_batch_size in (None, 4, 20):
          attributions, delta = cond.attribute(
              test_input,
              baselines=baselines,
              target=0,
              n_steps=500,
              method="gausslegendre",
              additional_forward_args=additional_args,
              internal_batch_size=internal_batch_size,
              return_convergence_delta=True,
          )
          delta_condition = (delta.abs() < 0.01).all()
          self.assertTrue(
              delta_condition,
              "Sum of attributions does {}"
              " not match the difference of endpoints.".format(delta),
          )
  
          assertTensorTuplesAlmostEqual(
              self, attributions, expected_conductance, delta=0.1
          )
  
imports: import unittest
  from typing import Any, cast, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType
  from captum.attr._core.layer.layer_conductance import LayerConductance
  from tests.attr.helpers.conductance_reference import ConductanceReference
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 16 for calling the Test obj's underlying member method code with example usage: obj._conductance_test_assert(model, target_layer, test_input, expected_conductance, baselines, additional_args) without return types